LAUSR

Eddy covariance (EC) provides ecosystem-scale estimates of photosynthesis (Ph) and evapotranspiration (ET; the sum of plant transpiration [T] and evaporation [Es]). Separating ET into its components is becoming necessary for linking plant-water use strategies to environmental variability. Based on optimality principles, a data-model based approach for partitioning ET was proposed and independently tested. Short-term responses of canopy-scale internal leaf-to-ambient CO2 (χ) were predicted based on a big-leaf representation of the canopy accounting for the influence of boundary-layer conductance. This representation allowed investigating stomatal behavior in accordance with the Ph estimates. With the objective of minimizing the carbon cost of transpiration, a novel optimization approach was implemented to develop solutions for an optimal stomatal conductance model as the basis to derive T. The Es was then calculated as a residual between the observed ET and modeled T. The proposed method was applied to long-term EC measurements collected above a Mediterranean tree-grass ecosystem. Estimated Es agreed with independent lysimeter measurements (r = 0.69). They also agreed with other partitioning methods derived from similarity theory and conditional sampling applied to turbulence measurements. These similarity schemes appeared to be sensitive to different χ parameterization. Measured Es was underestimated by 30% when χ was assumed constant (= 0.8). Diel and seasonal χ patterns were characterized in response to soil dryness. A surprising result was a large Es/ET throughout the seasons. The robustness of the results provides a new perspective on EC ET partitioning, which can be utilized across a wide range of climates and biomes.